Fix to install on Python 3.12

Author: basnijholt

adaptive/learner/base_learner.py

@@ -25,7 +25,6 @@ def uses_nth_neighbors(n: int):
 
     Examples
     --------
-
     The next function is a part of the `curvature_loss_function` function.
 
     >>> @uses_nth_neighbors(1)
@@ -57,6 +56,7 @@ def uses_nth_neighbors(n: int):
     ...         return loss * 100
     ...
     ...     return loss
+
     """
 
     def _wrapped(loss_per_interval):
@@ -88,6 +88,7 @@ class BaseLearner(abc.ABC):
     -----
     Subclasses may define a ``plot`` method that takes no parameters
     and returns a holoviews plot.
+
     """
 
     data: DataType
@@ -102,6 +103,7 @@ def tell(self, x, y):
         ----------
         x : A value from the function domain
         y : A value from the function image
+
         """
         self.tell_many([x], [y])
 
@@ -112,21 +114,23 @@ def tell_many(self, xs, ys):
         ----------
         xs : Iterable of values from the function domain
         ys : Iterable of values from the function image
+
         """
         for x, y in zip(xs, ys):
             self.tell(x, y)
 
     @abc.abstractmethod
     def tell_pending(self, x):
         """Tell the learner that 'x' has been requested such
-        that it's not suggested again."""
+        that it's not suggested again.
+        """
 
     @abc.abstractmethod
     def remove_unfinished(self):
         """Remove uncomputed data from the learner."""
 
     @abc.abstractmethod
-    def loss(self, real=True):
+    def loss(self, real: bool = True):
         """Return the loss for the current state of the learner.
 
         Parameters
@@ -135,6 +139,7 @@ def loss(self, real=True):
             If False, return the "expected" loss, i.e. the
             loss including the as-yet unevaluated points
             (possibly by interpolation).
+
         """
 
     @abc.abstractmethod
@@ -149,6 +154,7 @@ def ask(self, n, tell_pending=True):
             If True, add the chosen points to this learner's
             `pending_points`. Set this to False if you do not
             want to modify the state of the learner.
+
         """
 
     @abc.abstractmethod
@@ -162,7 +168,6 @@ def _set_data(self, data: Any) -> None:
     @abc.abstractmethod
     def new(self):
         """Return a new learner with the same function and parameters."""
-        pass
 
     def copy_from(self, other):
         """Copy over the data from another learner.
@@ -171,6 +176,7 @@ def copy_from(self, other):
         ----------
         other : BaseLearner object
             The learner from which the data is copied.
+
         """
         self._set_data(other._get_data())
 
@@ -184,6 +190,7 @@ def save(self, fname, compress=True):
         compress : bool, default True
             Compress the data upon saving using 'gzip'. When saving
             using compression, one must load it with compression too.
+
         """
         data = self._get_data()
         save(fname, data, compress)
@@ -198,6 +205,7 @@ def load(self, fname, compress=True):
         compress : bool, default True
             If the data is compressed when saved, one must load it
             with compression too.
+
         """
         with suppress(FileNotFoundError, EOFError):
             data = load(fname, compress)
@@ -228,6 +236,7 @@ def to_dataframe(
         Returns
         -------
         pandas.DataFrame
+
         """
 
     @abc.abstractmethod

adaptive/learner/integrator_learner.py

@@ -71,8 +71,7 @@ class DivergentIntegralError(ValueError):
 
 
 class _Interval:
-    """
-    Attributes
+    """Attributes
     ----------
     (a, b) : (float, float)
         The left and right boundary of the interval.
@@ -114,6 +113,7 @@ class _Interval:
     refinement_complete : depth, optional
         If true, all the function values in the interval are known at `depth`.
         By default the depth is the depth of the interval.
+
     """
 
     __slots__ = [
@@ -210,7 +210,8 @@ def update_heuristic_err(self, value: float) -> None:
         """Sets the error of an interval using a heuristic (half the error of
         the parent) when the actual error cannot be calculated due to its
         parents not being finished yet. This error is propagated down to its
-        children."""
+        children.
+        """
         self.err = value
         for child in self.children:
             if child.depth_complete or (
@@ -253,7 +254,8 @@ def update_ndiv_recursively(self) -> None:
 
     def complete_process(self, depth: int) -> tuple[bool, bool] | tuple[bool, np.bool_]:
         """Calculate the integral contribution and error from this interval,
-        and update the done leaves of all ancestor intervals."""
+        and update the done leaves of all ancestor intervals.
+        """
         assert self.depth_complete is None or self.depth_complete == depth - 1
         self.depth_complete = depth
 
@@ -341,8 +343,7 @@ def __repr__(self) -> str:
 
 class IntegratorLearner(BaseLearner):
     def __init__(self, function: Callable, bounds: tuple[int, int], tol: float) -> None:
-        """
-        Parameters
+        """Parameters
         ----------
         function : callable: X → Y
             The function to learn.
@@ -373,6 +374,7 @@ def __init__(self, function: Callable, bounds: tuple[int, int], tol: float) -> N
             Returns whether the `tol` has been reached.
         plot : hv.Scatter
             Plots all the points that are evaluated.
+
         """
         self.function = function  # type: ignore
         self.bounds = bounds
@@ -383,7 +385,7 @@ def __init__(self, function: Callable, bounds: tuple[int, int], tol: float) -> N
         self.pending_points = set()
         self._stack: list[float] = []
         self.x_mapping: dict[float, SortedSet] = defaultdict(
-            lambda: SortedSet([], key=attrgetter("rdepth"))
+            lambda: SortedSet([], key=attrgetter("rdepth")),
         )
         self.ivals: set[_Interval] = set()
         ival = _Interval.make_first(*self.bounds)
@@ -557,7 +559,7 @@ def done(self):
         )
 
     @cache_latest
-    def loss(self, real=True):
+    def loss(self, real: bool = True):
         return abs(abs(self.igral) * self.tol - self.err)
 
     def plot(self):
@@ -602,6 +604,7 @@ def to_dataframe(  # type: ignore[override]
         ------
         ImportError
             If `pandas` is not installed.
+
         """
         if not with_pandas:
             raise ImportError("pandas is not installed.")
@@ -638,6 +641,7 @@ def load_dataframe(  # type: ignore[override]
             Name of the input value, by default "x"
         y_name : str, optional
             Name of the output value, by default "y"
+
         """
         raise NotImplementedError